Estimation of pain intensity in emergency medicine: A validation study
Bibliographic record
Abstract
This study was designed to estimate the validity of an 11-point verbal numerical rating scale (VNRS) and a 100 Unit (U) plasticized visual analogue scale (VASp) using a 100mm paper visual analogue scale (VAS) as a gold standard, to recommend the best method of reporting the intensity of acute pain in an emergency department (ED). A convenience sample of 1176 patients with acute pain were recruited in the ED of a teaching hospital. Patients >18 years and able to use the different scales were included. Scales were presented randomly. Results were converted to a 0-100 U scale and validity was quantified using the Bland-Altman method and the intra-class correlation (ICC). The limits of acceptability were previously set for the limits of agreement at +/-20 U, with a constant bias. The Bland-Altman method revealed a small bias of -4 U for the VNRS and +1 U for VASp. However, the bias of the VNRS varied with the intensity of pain from -10 to +1 U. The limits of agreement between the VNRS&VAS and the VASp&VAS were -25; +17 U and -17; +18 U, respectively. The ICC was excellent between the VNRS&VAS (0.88) and the VASp&VAS (0.92). In conclusion, the VASp has a small bias, acceptable limits of agreement and an excellent intra-class correlation. It is probably a valid tool to estimate acute pain in the ED. However, the VNRS is less valid in that context because of its wide limits of agreement and variable bias (mainly in lower scores).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".